Voltage Network Research Articles

Improving the Control Strategy of the DVR Compensator Based on an Adaptive Notch Filter with an Optimized PD Controller Using the IGWO Algorithm

One of the objectives of electrical distribution networks is to provide customers with access to high‐quality electricity. Because any disruptions in these systems result in voltage disorders, different devices are employed to offset these disruptions on consumers who are more susceptible. One of the most important and contemporary pieces of equipment that is connected in series with the network is dynamic voltage restoration (DVR), which shields delicate loads from network voltage issues by injecting the proper voltage. This article presents a DVR control scheme optimized with improved grey wolf optimization (IGWO) that uses a proportional derivative (PD) controller and adaptive notch filter (ANF). The output LC filter’s resistance has been removed, and the control system has actively engaged in oscillation damping in order to accelerate dynamic responsiveness and lower system losses. The major component of the voltage, which comprises its frequency, amplitude, and phase, is extracted using ANF. The capacitor current of the output filter in this structure is fed back to the control system and from the current mode control in the inner loop to boost stability. Owing to the occasionally complex dynamic behavior in distribution networks, particularly during a fault, the system’s frequency response has been altered and response speed has been accelerated using the PD controller. This kind of controller is distinguished by its accurate functioning in the presence of frequency deviations and its swifter dynamic reaction in the face of voltage swell and sag. In order to improve the THD and voltage sag indicators of the sensitive load, the PD coefficients were adjusted using the IGWO algorithm. As a consequence, the simulation results demonstrated that the suggested controller performed better than traditional controllers.

Lost-Phase and Short-Circuit Impacts on Electrical Network Managed by a Dual IPCs 30P15

This work evaluates the impact of short-circuit and the phase-lost in a particular transmission network located in south division in Cameroon (Central Africa). This network is precisely located between two towns called Songloulou and Logbaba (douala Town). The chosen network is modelled in Matlab Simulink software. Further, the network currents and voltages undergo non periodical distortions when short-circuit and phase lost come into play. Those distortions are deleted when dual controllers’ system is introduced and the network is stabilized. It appears that dual IPCs (interphase power controllers) present good results. However, the dual system which is a combination of two IPCs improves the network stability. The investigation of dual IPCs’ system uses the phase shift method.

An advanced secondary voltage control strategy for future power systems

The network voltage control together with the regulation of reactive power, has always been recognized as vital feature of power systems. It is aimed at containing the bus voltage within acceptable limits for guaranteeing system security, first and then assessing power quality. The voltage control is a key field in which several Transmission System Operators are currently investing resources to be well prepared in managing power systems with a high share of renewable energy sources, overcoming the drawbacks of the existing architectures. As is well known the voltage control is realized through a hierarchical structure, which is based on classical primary control, secondary control, and tertiary optimization. The secondary control is concerned with the regional level, implying proper coordination at both the geographical and temporal level. Due to its central role in power system operation, the secondary voltage control cannot be excluded from the effects of the dramatic changes that power systems are experiencing worldwide, even though regulators work by implementing well consolidated methodologies and strategies which show excellent performances. The motivation for this study in fact relies on the observation of some oscillatory phenomena in some areas of the Italian power system characterized by a high number of regulating resources highly coupled each other on the basis of the grid topology: despite these oscillatory phenomena are not so frequent, with the increasing of regulating resources enslaved by secondary regulation (such as renewable-based power plants) could potentially be worsened in the near future. Thus, in this paper, an optimal control strategy is proposed by tailoring a proper linear quadratic integral control which allows guaranteeing the desired profile for pilot buses voltage, ensuring a time response of aperiodic type without any oscillations. The aim of the proposed approach is to preserve the strengths of the existing secondary voltage regulation while enhancing the overall stability so enabling the progressive large penetration of renewable energy sources-based power plants and minimize the occurrence of oscillatory phenomena especially in highly electrically coupled areas. The robustness of the proposed regulation is verified first with respect to the WSCC 9-bus test system and successively on an actual regional area of Italian transmission network.

STUDY OF PHASE VOLTAGE AND LOAD SYMMETRATION IN ACTIVE-ADAPTIVE DISTRIBUTION ELECTRIC NETWORKS

Networks of 0.4 kV are characterized by a large unbalance of loads in phases. Current unbalance leads to voltage unbalance and additional losses of electrical energy. As a result, the voltage at the consumer may not meet the quality standards of electrical energy. In addition, due to unbalance, the service life of electrical equipment is reduced. Since the effect of stress balancing significantly depends on the place of balancing loads on the line, the paper proposes to determine the places of balancing loads by solving a multicriteria optimization problem. The paper proposes an objective function that minimizes active power losses and contains the total index of active power losses and indices of voltage unbalance coefficients in the negative and zero sequence.
 
 The purpose of the study is to obtain an effective objective function for determining the places of balancing loads and voltages in the network, which ensures a minimum of active power losses and the values of the voltage unbalance factors within the required limits; conduct a study of balancing loads and voltages depending on the places of balancing.
 
 Materials and methods. In the work, methods for calculating electrical networks were used, taking into account voltage losses and active power. To study the places of balancing loads and voltages, the method of multicriteria optimization with restrictions was used. The study of the objective function was carried out on a mathematical model of a low voltage overhead line. All calculations were carried out in MATLAB.
 
 Research results. A review and analysis of modern tools and methods for balancing loads and voltages in low voltage networks has been carried out. As a result of the analysis, it was concluded that there is no algorithm for determining the places of load balancing in low-voltage networks that provide minimal active power losses and the values of the voltage unbalance factors within the required limits. The task of finding places for balancing loads and voltages is a multiobjective optimization problem with constraints. Therefore, an objective function was proposed that minimizes active power losses in the network and contains the total index of active power losses and indices of voltage unbalance coefficients for the reverse and zero sequence. To study the proposed objective function, a model overhead line of a 0.4 kV network with specified phase loads and voltages was used. For the model line, the calculation of active power losses and the values of the voltage unbalance coefficients in the initial mode before balancing was carried out. All calculations were carried out for each phase separately. At the first stage, the calculation of the sensitivity coefficients of active power losses and the sensitivity coefficients for the voltage unbalance coefficients was carried out. To study the balancing of loads, nodes were selected that have the maximum values of the sensitivity coefficients. It follows from the calculation results that the best effect from balancing is observed when balancing loads simultaneously in two nodes: in the node with the highest value of the total sensitivity factor of active power losses, and in the node with the maximum value of the phase sensitivity factor of active power losses. When balancing loads in only one of the nodes, the most optimal of the selected ones will be the node most remote from the TS. We also obtained weight coefficients that provide a minimum of the objective function.
 
 Conclusions. The proposed objective function is effective for determining the places of load and voltage balancing in low voltage networks. In this case, the best effect is observed when balancing loads in nodes that have the highest values of the sensitivity coefficients of total active power losses and by phases. The node most remote from the TP will be more optimal. When balancing loads in places determined using the proposed objective function, it is possible to reduce power losses and ensure the values of the unbalance coefficients in the nodes on the line less than the maximum allowable value.

Multiple control strategies for smart photovoltaic inverter under network voltage fluctuations and islanded operation

This article proposes a central control system that communicates with both grid-tied and off-grid control systems to offer various control strategies for operating a smart photovoltaic (PV) inverter. The target is to connect two sets of PV panels and one set of battery storage unit to either a 440 V/60 Hz utility grid or to feed local loads at 380 V/50 Hz using a smart inverter. When the smart PV inverter is connected to the grid, on the one hand, it injects fixed and programmed active power into the grid under all operating conditions, both normal and critical conditions, and on the other hand, by reciprocal exchanging reactive power with the grid, it addresses balanced and unbalanced fluctuations of the grid voltage. Also, when the smart PV inverter is disconnected from the grid, it can operate as an islanded mode to feed time-varying three-phase loads. Nonlinear simulations of the time domain in MATLAB-Simulink software show that the set of control strategies offered for the smart PV inverter not only by controlling the active power leads to the sale of electricity to the grid, but also by controlling the reactive power problems on the grid side such as voltage drop as a result of single-line-to-ground (SLG) or line-to-line-to-ground (2LG) faults, balanced grid voltage fluctuations such as voltage sag-swell and unbalanced voltage conditions will be resolved. Also, variable three-phase loads are fed well in off-grid mode.

Energy management of distribution network with inverter‐based renewable virtual power plant considering voltage security index

AbstractVirtual power plants (VPP) with resources and storages are able to control the active power of the network. They are also connected to the network through an inverter, which is capable of controlling reactive power. Therefore, it is expected that the optimal use of inverter‐based VPP can play an effective role in improving the economic and technical status of the distribution network. So, the operation of a smart distribution system is presented in this paper by considering inverter‐based VPPs constrained to the operator's measures. The weighted sum of expected energy loss (EEL) and voltage security index (VSI) is minimized while considering AC optimal power flow equations, restrictions of network's security, and operating model of the inverter‐based VPPs. Uncertainties with an origin of the amount of demand, renewable energy, and parameters of mobile energy storage are also discussed. The uncertainties are modelled using a stochastic optimization approach relying on the unscented transformation (UT). Evaluating inverter‐based VPP performance, providing models of flexible resources such as responsive loads and mobile storages, checking network voltage security status, and modelling uncertainties using the UT method are among the innovations of this study. According to the results, it is demonstrated that the technical situation of the distribution system is improved with the help of optimal management of the VPP. With energy management of the inverter‐based VPP, the suggested design has succeeded to enhance the operating status (voltage security) of the system by approximately 33–73% (12%) in comparison to power flow studies.

DEVICE FOR CONTROLLING PARAMETERS OF ACCUMULATOR BATTERIES AND THE CORRESPONDING DIRECT CURRENT NETWORK

Mobile devices for information, control and telemetry systems are powered by mobile generators through AC to DC converters, by batteries or, if possible, directly by solar panels. Solar panels typically work in a system that involves the usage of batteries to keep the systems running when the solar panels are not working or not providing enough power. The batteries can be recharged from the panels themselves if the solar panels have sufficient power or from an external direct current source. Also, for some mobile devices, power systems are used only with batteries, which are recharged from generators or, if possible, from standard electrical networks. All these options of power supply systems for mobile devices require operational control of battery parameters and corresponding direct current networks. The paper proposes a device for monitoring the following parameters: voltage of the direct current network from which the mobile device is powered or the batteries are charged; battery charge current control; control of insulation resistance of direct current circuits; control of the voltage of chargers; monitoring the status of chargers; protection of batteries from deep discharge; control of the state of switching nodes. The device is implemented as a two-processor system based on STM32F103 microcontrollers. A non-contact sensor of the LEM LA 100-P type is used to measure the direct current, which generates an analog signal proportional to the value of the direct current. This signal is passed to a 16- bit analog-to-digital converter. Given that these microcontrollers have integrated 12-bit analog-to- digital converters, an external 16-bit analog-to-digital converter of the ADS1115 type is used to ensure the necessary accuracy of direct current and voltage measurement, which transmits information to the basic processor via the I2C interface. The basic processor implements the main operating modes of the device, and the local processor provides information exchange with the general mobile power supply system through the RS-485 interface. The device is equipped with an indication system based on an LCD indicator of the VS1602A type and functionally programmable single LED indicators, a local keyboard for selecting control modes, a USB port for connecting additional modules and a SWD port for programming the Flash memory of microcontrollers and debugging programs in real time. During operation, parameters of non-standard events are stored in the device's non-volatile memory. The software of the basic and local processors has been developed, which ensures the functioning of the device in basic modes and performs periodic self-diagnosis of the device. The obtained results can be used in scientific research and in the design of real automated power systems for mobile information systems. Keywords: power systems of mobile information systems, batteries, battery charging, microcontroller, LCD indicator, I2C interface, USB interface, RS-485 interface, SWD interface.

A voltage control method for distribution networks based on TCN and MPGA under cloud edge collaborative architecture

A distribution network voltage control method based on TCN and MPGA under cloud edge collaborative architecture is proposed to address the issues of heavy computational burden and inability to balance economic efficiency in traditional decentralized and centralized voltage control methods. Firstly, under the cloud edge collaboration architecture, the power prediction model based on time convolutional networks is trained in the cloud, and the edge predicts the load demand and new energy active output in each region based on the TCN Attention power prediction model issued by the cloud. Then, considering safety and economy comprehensively, with the goal of minimizing the daily network loss and voltage regulation cost in the distribution network, the Pareto solution set for the optimal operation point of each voltage regulation equipment in the entire distribution network was solved based on an improved multi population genetic algorithm at the distribution cloud main station. Finally, the optimal distribution network voltage control scheme is obtained based on the Pareto solution set. Based on the simulation system, experimental testing was conducted on the proposed method, and the results showed that its daily voltage regulation cost and network loss were 863.4 yuan and 43.73 MW h, respectively. The average absolute percentage error of the prediction model was 4.51 %, which was superior to other comparative methods.

Multi-objective Collaborative Optimization Method for Intelligent Distribution Network Operation and Maintenance

Most of the conventional multi-objective coordination methods for active distribution network operation are designed based on the principle of weighted random algorithm, which has limited application scope and cannot simplify the algorithm and reduce the complexity of the solution process, resulting in poor stability of active distribution network lines and potential faults. Based on this, this algorithm is introduced to carry out multi-objective collaborative optimization for intelligent distribution network operation. Firstly, the node types for the intelligent distribution network are defined and the power flow of the distribution network is analyzed. Secondly, a multi-objective configuration model for the lower level of the operation is constructed to maximize the distribution network investment and operation benefit index. A multi-objective collaborative optimization algorithm based on an enhanced particle swarm optimization algorithm is designed to reduce the operation cost of the distribution network and enhance the security and stability of the distribution network system during operation. The simulation test results show that after the application of the new method, the intelligent distribution network voltage stability index Vm is always less than 1, and the line stability has been significantly improved.

Real-time Strategy for Voltage Regulation of Distribution Networks Considering the Incomplete Response of Distributed Resources

To solve the problem of nodes whose voltage exceeds the security constraint in the scenario of massive distributed generation access, a real-time strategy for voltage regulation of the distribution network considering the incomplete response of distributed resources is proposed. The strategy includes two parts: a real-time strategy for voltage regulation of the distribution network and the improvement of the strategy to deal with the incomplete response of distributed resources. Firstly, the real-time voltage autonomy of the distribution network is realized through the distributed resource strategy for voltage regulation; Then, aiming at the incomplete response of distributed resources, the strategy for voltage regulation is improved, and the margin iteration and feedback regulation are added; Finally, based on the IEEE33 node model, we verify the strategy for voltage regulation which is proposed in this paper. The results of the experiment show that the strategy for voltage regulation can effectively solve the problem of distribution network voltage out of limit, and has good anti-interference ability for the incomplete response of distributed resources.

Simulations of the operation of the fast light innovative regional train from “Serbian Railways” in traction and electric braking mode

In the paper, the MATLAB-Simulink model of simulation of operation of the fast light innovative regional train from “Serbian Railways” in traction and braking mode is exposed where changes are observed: stator currents of three-phase traction motors, traction electric motor speeds and electric multiple unit, electromagnetic torque on the rotor shaft of the traction electric motor and diirect current bus voltage. The model allowed review of the listed parameters for: different allowed values of contact network voltage and total voltage distortion at the place of connection of the electric multiple unit to the contact network, different mechanical loads of electric multiple unit and traction electric motor and different train speeds and rotation of traction electric motors. Appropriate conclusions were made through the analysis of the simulation results obtained.

Management of prosumers using dynamic export limits and shared Community Energy Storage

Integration of Distributed Energy Resources (DERs) can introduce challenges such as Over-Voltage (OV) and line congestion in distribution networks. Recently, the concept of dynamic export limits as well as Community Energy Storage (CES) have gained attention as potential solutions for these challenges. Within these concepts, this paper presents a novel framework where the Distribution Network Service Provider (DNSP) provides prosumers with dynamic export limits for networks with a CES. Moreover, this framework enables prosumers (through leasing) and the DNSP to collaboratively manage the CES. In each time step, the DNSP calculates the charging/discharging power for the unleased portion of the CES and collects data on the expected export and charging/discharging power from prosumers leasing the CES. Subsequently, while considering network voltage and current limits and a fairness objective, the DNSP computes and communicates dynamic export limits to prosumers, allowing them to manage their assets accordingly. The performance of the proposed framework is evaluated via various case studies conducted on a real-world LV test feeder with 49 prosumers. The results emphasize the critical role of dynamic export limits in resolving OV and congestion issues in DER-rich networks. Furthermore, the CES proves to be effective in reducing active power curtailment and increasing total generation, with a notable increase of 4.6%.

Design of Power Supply Based on Inductive Power Transfer System for Medium Voltage Direct Current Sensor

This paper proposes a medium voltage direct current (MVDC) sensor power supply method based on inductive power transfer (IPT). Given that MVDC distribution networks transmit power at high voltages (several tens of kV), control through sensors is necessary to prevent exacerbating MVDC distribution network accidents. Moreover, these high voltages in MVDC distribution networks mean that high voltage insulation is required between the sensor and the distribution line and for any power supply device electrically connected to the sensor. Therefore, this paper proposes a safe and reliable power supply method using the principle of IPT to maintain a suitable insulation distance between the distribution network and the current sensor supply line. After proposing and designing a transmitter/receiver pad and IPT system by considering the current sensor specifications, a 50-W experimental prototype is developed. The experiments demonstrated that the proposed IPT system can resolve concerns about the breakdown of insulation between distribution networks and power supply lines.

Modeling and Energy-Optimal Control for Freight Trains based on Data-Driven Approaches

This paper focuses on the energy optimization problem of traction substations. The paper addresses the difficulty of considering time-varying parameters and environmental characteristics of freight train in mechanism modeling. In response, a new optimization method called “Modeling and Energy-Optimal Control for Freight Trains based on Data-Driven Approaches” is proposed. Firstly, a data-driven model considering the “Train-Track-Power grid” (TTP) is constructed based on recorded data. The energy optimization problem is regarded as a finite Markov decision process. A dynamic programming (DP) algorithm is utilized to optimize the train’s control force. The data-driven model is then used to solve for the train’s speed curve, traction substation power, and contact network voltage. Secondly, a multi-input multi-output self-organizing fuzzy neural network (MIMO-SOFNN) model is designed during the modeling process, and an Levenberg–Marquardt algorithm with self-adaption damping factor (SA-LM) is proposed for model parameter learning. Finally, experimental analysis is conducted to validate the high accuracy of the MIMO-SOFNN model compared to five other models. The effectiveness of the SA-LM algorithm is also verified through a comparison with five other algorithms. In the energy optimization experiments, when compared with the actual operation data of a freight railway company in China, the proposed energy optimization method in this paper reduces the energy consumption of the traction substation by 34.8%.

Energy model based sensorless estimation method for operational temperature of braking resistor onboard metro vehicles

PurposeThis study aims to improve the availability of regenerative braking for urban metro vehicles by introducing a sensorless operational temperature estimation method for the braking resistor (BR) onboard the vehicle, which overcomes the vulnerability of having conventional temperature sensor.Design/methodology/approachIn this study, the energy model based sensorless estimation method is developed. By analyzing the structure and the convection dissipation process of the BR onboard the vehicle, the energy-based operational temperature model of the BR and its cooling domain is established. By adopting Newton's law of cooling and the law of conservation of energy, the energy and temperature dynamic of the BR can be stated. To minimize the use of all kinds of sensors (including both thermal and electrical), a novel regenerative braking power calculation method is proposed, which involves only the voltage of DC traction network and the duty cycle of the chopping circuit; both of them are available for the traction control unit (TCU) of the vehicle. By utilizing a real-time iterative calculation and updating the parameter of the energy model, the operational temperature of the BR can be obtained and monitored in a sensorless manner.FindingsIn this study, a sensorless estimation/monitoring method of the operational temperature of BR is proposed. The results show that it is possible to utilize the existing electrical sensors that is mandatory for the traction unit’s operation to estimate the operational temperature of BR, instead of adding dedicated thermal sensors. The results also validate the effectiveness of the proposal is acceptable for the engineering practical.Originality/valueThe proposal of this study provides novel concepts for the sensorless operational temperature monitoring of BR onboard rolling stocks. The proposed method only involves quasi-global electrical variable and the internal control signal within the TCU.

Analysis of voltage and frequency stability of electric power system network with photovoltaic-based generation penetration

The operation of Distributed Generation (DG) with renewable energy sources integrated with distribution networks through microgrids poses challenges in terms of operation and control. If this is left unchecked, it can have a negative impact on system security and reliability in terms of voltage and frequency stability that will be disrupted because of frequent variations in power production and loading levels. This study investigates voltage and frequency stability in microgrids because of the penetration of DG with photovoltaic (PV) renewable energy sources in the power system using the Virtual Synchronous Generator (VSG) control technique. The VSG is a control alteration that enhances the capabilities of the power system so that voltage and frequency stability can be preserved and improved. The VSG control method with additional damping controllers that increase inertia with additional virtual inertia is used to simulate the speed of restoration of voltage and frequency stability of the power system due to the penetration of PV-based power plants. The simulation results show that at the time of penetration of PV-based power plants in the power system, there is a momentary instability in voltage and frequency, but it is immediately dampened by VSG control and can be quickly restored so that the stability of voltage and frequency is maintained.

Distribution network voltage analysis with data-driven electric vehicle load profiles

The number of electric vehicles (EVs) is expected to increase significantly in the near future, which will affect the operation of future power networks. This study investigates the impact of EVs on the distribution network voltage profile by collecting measurements from residential, slow commercial, and fast public EV charging stations throughout the annual cycle. It is revealed from the data that annual utilization factors for residential, slow commercial, and fast public charging stations are 89%, 55%, and 24%, respectively. The optimal power flow solutions have been presented without the EV and with EV (home, commercial, and public) load profiles. Simulation results identify the voltage violation's extent, instant, and duration that may affect the future distribution grid. Most voltage violations happen in the evening, weekday afternoon, and midday for residential, slow commercial, and fast public EV charging stations, respectively. Furthermore, multiple enabling technologies have been demonstrated to reduce voltage violations, such as coordinated charging (reduced voltage violation by 50%), renewable charging (by 50%), and combinations of multiple types of customers (by 30%). This study presents some key insights into future power system operational scenarios with widespread EV charging stations that can be managed by multiple enabling technologies.

Impact of DGs on the Reactive Power-Supported Optimal EDN for Profit Maximisation

Significant issues such as high-Power Loss (PLoss) and drop in node voltages in Electric Distribution Networks (EDNs) can be well mitigated using renowned techniques such as Alteration of Electric Distribution Network Switches (AEDNS), Optimal Capacitor Support (OCS), and Integration of Dispersed Generation (IDG), which are identified as the most economical and efficient approaches. This study presents the optimisation of AEDNS with and without OCS considering four different scenarios to maximise the profit through reduction in PLoss, which is regarded as the first-step process. To further increase profit, IDG was integrated into the EDNs after the combined optimisation of OCS and AEDNS. In this work, Levy Flight Mechanism (LFM) was incorporated into the Seagull Optimisation Algorithm (SOA) and applied to solve the objective function based on economics. The effectiveness of the presented methodology was evaluated and confirmed using a real 59-bus in Cairo, Egypt, EDN, as well as a conventional 33-bus test system. For each scenario, the PLoss reductions and net profit of the proposed methodology were contrasted with those obtained from previously reported approaches. The collected findings show that by optimising AEDNS, OCS, and IDG, the established methodology effectively yields more economic gain for all scenarios.

Assessment of hybrid transfer learning method for forecasting EV profile and system voltage using limited EV charging data

The number of electric vehicles (EV) is increasing exponentially, significantly affecting the planning and operation of future electricity grids, albeit the availability of EV data is very limited to perform power system studies. To overcome the poor forecasting accuracy associated with limited available data, this research proposes a time-series-based hybrid transfer learning forecasting approach, namely CNN-BiLSTM (Convolutional Neural Network – Bidirectional Long Short-Term Memory) to forecast EV charging profile. Additionally, the proposed algorithm has been applied to forecast network voltage from the EV data without performing power flow. EV charging demand datasets collected over a year for residential, slow commercial, and fast commercial charging stations and their corresponding voltage profiles have been used to test the effectiveness of the proposed hybrid transfer learning framework. The results confirm the improved accuracy of the proposed hybrid CNN-BiLSTM model compared to the conventional CNN model, newly created models in predicting the EV charging demand and voltage profiles.

Optimal Dispatch Strategy for a Distribution Network Containing High-Density Photovoltaic Power Generation and Energy Storage under Multiple Scenarios

To better consume high-density photovoltaics, in this article, the application of energy storage devices in the distribution network not only realizes the peak shaving and valley filling of the electricity load but also relieves the pressure on the grid voltage generated by the distributed photovoltaic access. At the same time, photovoltaic power generation and energy storage cooperate and have an impact on the tidal distribution of the distribution network. Since photovoltaic output has uncertainty, the maximum photovoltaic output in each scenario is determined by the clustering algorithm, while the storage scheduling strategy is reasonably selected so the distribution network operates efficiently and stably. The tidal optimization of the distribution network is carried out with the objectives of minimizing network losses and voltage deviations, two objectives that are assigned comprehensive weights, and the optimization model is constructed by using a particle swarm algorithm to derive the optimal dispatching strategy of the distribution network with the cooperation of photovoltaic and energy storage. Finally, a model with 30 buses is simulated and the system is optimally dispatched under multiple scenarios to demonstrate the necessity of conducting coordinated optimal dispatch of photovoltaics and energy storage.